Initial Space setup

scripts/trackio_tonic/README.md

@@ -0,0 +1,46 @@
+---
+title: Trackio Tonic
+emoji: 🐠
+colorFrom: indigo
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.38.0
+app_file: app.py
+pinned: true
+license: mit
+short_description: trackio for training monitoring
+---
+
+# Trackio Experiment Tracking
+
+A Gradio interface for experiment tracking and monitoring.
+
+## Features
+
+- Create and manage experiments
+- Log training metrics and parameters
+- View experiment details and results
+- Update experiment status
+
+## Usage
+
+1. Create a new experiment using the "Create Experiment" tab
+2. Log metrics during training using the "Log Metrics" tab
+3. View experiment details using the "View Experiments" tab
+4. Update experiment status using the "Update Status" tab
+
+## Integration
+
+To connect your training script to this Trackio Space:
+
+```python
+from monitoring import SmolLM3Monitor
+
+monitor = SmolLM3Monitor(
+    experiment_name="my_experiment",
+    trackio_url="https://huggingface.co/spaces/Tonic/trackio_test_2",
+    enable_tracking=True
+)
+```
+
+Visit: https://huggingface.co/spaces/Tonic/trackio_test_2 

scripts/trackio_tonic/app.py

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+"""
+Trackio Deployment on Hugging Face Spaces
+A Gradio interface for experiment tracking and monitoring
+"""
+
+import gradio as gr
+import os
+import json
+import logging
+from datetime import datetime
+from typing import Dict, Any, Optional
+import requests
+import plotly.graph_objects as go
+import plotly.express as px
+import pandas as pd
+import numpy as np
+
+# Setup logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class TrackioSpace:
+    """Trackio deployment for Hugging Face Spaces using HF Datasets"""
+    
+    def __init__(self, hf_token: Optional[str] = None, dataset_repo: Optional[str] = None):
+        self.experiments = {}
+        self.current_experiment = None
+        
+        # Get dataset repository and HF token from parameters or environment variables
+        self.dataset_repo = dataset_repo or os.environ.get('TRACKIO_DATASET_REPO', 'tonic/trackio-experiments')
+        self.hf_token = hf_token or os.environ.get('HF_TOKEN')
+        
+        logger.info(f"🔧 Using dataset repository: {self.dataset_repo}")
+        
+        if not self.hf_token:
+            logger.warning("⚠️ HF_TOKEN not found. Some features may not work.")
+        
+        self._load_experiments()
+        
+    def _load_experiments(self):
+        """Load experiments from HF Dataset"""
+        try:
+            if self.hf_token:
+                from datasets import load_dataset
+                
+                # Try to load the dataset
+                try:
+                    dataset = load_dataset(self.dataset_repo, token=self.hf_token)
+                    logger.info(f"✅ Loaded experiments from {self.dataset_repo}")
+                    
+                    # Convert dataset to experiments dict
+                    self.experiments = {}
+                    if 'train' in dataset:
+                        for row in dataset['train']:
+                            exp_id = row.get('experiment_id')
+                            if exp_id:
+                                self.experiments[exp_id] = {
+                                    'id': exp_id,
+                                    'name': row.get('name', ''),
+                                    'description': row.get('description', ''),
+                                    'created_at': row.get('created_at', ''),
+                                    'status': row.get('status', 'running'),
+                                    'metrics': json.loads(row.get('metrics', '[]')),
+                                    'parameters': json.loads(row.get('parameters', '{}')),
+                                    'artifacts': json.loads(row.get('artifacts', '[]')),
+                                    'logs': json.loads(row.get('logs', '[]'))
+                                }
+                    
+                    logger.info(f"📊 Loaded {len(self.experiments)} experiments from dataset")
+                    
+                except Exception as e:
+                    logger.warning(f"Failed to load from dataset: {e}")
+                    # Fall back to backup data
+                    self._load_backup_experiments()
+            else:
+                # No HF token, use backup data
+                self._load_backup_experiments()
+                
+        except Exception as e:
+            logger.error(f"Failed to load experiments: {e}")
+            self._load_backup_experiments()
+    
+    def _load_backup_experiments(self):
+        """Load backup experiments when dataset is not available"""
+        logger.info("🔄 Loading backup experiments...")
+        
+        backup_experiments = {
+            'exp_20250720_130853': {
+                'id': 'exp_20250720_130853',
+                'name': 'petite-elle-l-aime-3',
+                'description': 'SmolLM3 fine-tuning experiment',
+                'created_at': '2025-07-20T11:20:01.780908',
+                'status': 'running',
+                'metrics': [
+                    {
+                        'timestamp': '2025-07-20T11:20:01.780908',
+                        'step': 25,
+                        'metrics': {
+                            'loss': 1.1659,
+                            'grad_norm': 10.3125,
+                            'learning_rate': 7e-08,
+                            'num_tokens': 1642080.0,
+                            'mean_token_accuracy': 0.75923578992486,
+                            'epoch': 0.004851130919895701
+                        }
+                    },
+                    {
+                        'timestamp': '2025-07-20T11:26:39.042155',
+                        'step': 50,
+                        'metrics': {
+                            'loss': 1.165,
+                            'grad_norm': 10.75,
+                            'learning_rate': 1.4291666666666667e-07,
+                            'num_tokens': 3324682.0,
+                            'mean_token_accuracy': 0.7577659255266189,
+                            'epoch': 0.009702261839791402
+                        }
+                    },
+                    {
+                        'timestamp': '2025-07-20T11:33:16.203045',
+                        'step': 75,
+                        'metrics': {
+                            'loss': 1.1639,
+                            'grad_norm': 10.6875,
+                            'learning_rate': 2.1583333333333334e-07,
+                            'num_tokens': 4987941.0,
+                            'mean_token_accuracy': 0.7581205774843692,
+                            'epoch': 0.014553392759687101
+                        }
+                    },
+                    {
+                        'timestamp': '2025-07-20T11:39:53.453917',
+                        'step': 100,
+                        'metrics': {
+                            'loss': 1.1528,
+                            'grad_norm': 10.75,
+                            'learning_rate': 2.8875e-07,
+                            'num_tokens': 6630190.0,
+                            'mean_token_accuracy': 0.7614579878747463,
+                            'epoch': 0.019404523679582803
+                        }
+                    }
+                ],
+                'parameters': {
+                    'model_name': 'HuggingFaceTB/SmolLM3-3B',
+                    'max_seq_length': 12288,
+                    'use_flash_attention': True,
+                    'use_gradient_checkpointing': False,
+                    'batch_size': 8,
+                    'gradient_accumulation_steps': 16,
+                    'learning_rate': 3.5e-06,
+                    'weight_decay': 0.01,
+                    'warmup_steps': 1200,
+                    'max_iters': 18000,
+                    'eval_interval': 1000,
+                    'log_interval': 25,
+                    'save_interval': 2000,
+                    'optimizer': 'adamw_torch',
+                    'beta1': 0.9,
+                    'beta2': 0.999,
+                    'eps': 1e-08,
+                    'scheduler': 'cosine',
+                    'min_lr': 3.5e-07,
+                    'fp16': False,
+                    'bf16': True,
+                    'ddp_backend': 'nccl',
+                    'ddp_find_unused_parameters': False,
+                    'save_steps': 2000,
+                    'eval_steps': 1000,
+                    'logging_steps': 25,
+                    'save_total_limit': 5,
+                    'eval_strategy': 'steps',
+                    'metric_for_best_model': 'eval_loss',
+                    'greater_is_better': False,
+                    'load_best_model_at_end': True,
+                    'data_dir': None,
+                    'train_file': None,
+                    'validation_file': None,
+                    'test_file': None,
+                    'use_chat_template': True,
+                    'chat_template_kwargs': {'add_generation_prompt': True, 'no_think_system_message': True},
+                    'enable_tracking': True,
+                    'trackio_url': 'https://tonic-test-trackio-test.hf.space',
+                    'trackio_token': None,
+                    'log_artifacts': True,
+                    'log_metrics': True,
+                    'log_config': True,
+                    'experiment_name': 'petite-elle-l-aime-3',
+                    'dataset_name': 'legmlai/openhermes-fr',
+                    'dataset_split': 'train',
+                    'input_field': 'prompt',
+                    'target_field': 'accepted_completion',
+                    'filter_bad_entries': True,
+                    'bad_entry_field': 'bad_entry',
+                    'packing': False,
+                    'max_prompt_length': 12288,
+                    'max_completion_length': 8192,
+                    'truncation': True,
+                    'dataloader_num_workers': 10,
+                    'dataloader_pin_memory': True,
+                    'dataloader_prefetch_factor': 3,
+                    'max_grad_norm': 1.0,
+                    'group_by_length': True
+                },
+                'artifacts': [],
+                'logs': []
+            },
+            'exp_20250720_134319': {
+                'id': 'exp_20250720_134319',
+                'name': 'petite-elle-l-aime-3-1',
+                'description': 'SmolLM3 fine-tuning experiment',
+                'created_at': '2025-07-20T11:54:31.993219',
+                'status': 'running',
+                'metrics': [
+                    {
+                        'timestamp': '2025-07-20T11:54:31.993219',
+                        'step': 25,
+                        'metrics': {
+                            'loss': 1.166,
+                            'grad_norm': 10.375,
+                            'learning_rate': 7e-08,
+                            'num_tokens': 1642080.0,
+                            'mean_token_accuracy': 0.7590958896279335,
+                            'epoch': 0.004851130919895701
+                        }
+                    },
+                    {
+                        'timestamp': '2025-07-20T11:54:33.589487',
+                        'step': 25,
+                        'metrics': {
+                            'gpu_0_memory_allocated': 17.202261447906494,
+                            'gpu_0_memory_reserved': 75.474609375,
+                            'gpu_0_utilization': 0,
+                            'cpu_percent': 2.7,
+                            'memory_percent': 10.1
+                        }
+                    }
+                ],
+                'parameters': {
+                    'model_name': 'HuggingFaceTB/SmolLM3-3B',
+                    'max_seq_length': 12288,
+                    'use_flash_attention': True,
+                    'use_gradient_checkpointing': False,
+                    'batch_size': 8,
+                    'gradient_accumulation_steps': 16,
+                    'learning_rate': 3.5e-06,
+                    'weight_decay': 0.01,
+                    'warmup_steps': 1200,
+                    'max_iters': 18000,
+                    'eval_interval': 1000,
+                    'log_interval': 25,
+                    'save_interval': 2000,
+                    'optimizer': 'adamw_torch',
+                    'beta1': 0.9,
+                    'beta2': 0.999,
+                    'eps': 1e-08,
+                    'scheduler': 'cosine',
+                    'min_lr': 3.5e-07,
+                    'fp16': False,
+                    'bf16': True,
+                    'ddp_backend': 'nccl',
+                    'ddp_find_unused_parameters': False,
+                    'save_steps': 2000,
+                    'eval_steps': 1000,
+                    'logging_steps': 25,
+                    'save_total_limit': 5,
+                    'eval_strategy': 'steps',
+                    'metric_for_best_model': 'eval_loss',
+                    'greater_is_better': False,
+                    'load_best_model_at_end': True,
+                    'data_dir': None,
+                    'train_file': None,
+                    'validation_file': None,
+                    'test_file': None,
+                    'use_chat_template': True,
+                    'chat_template_kwargs': {'add_generation_prompt': True, 'no_think_system_message': True},
+                    'enable_tracking': True,
+                    'trackio_url': 'https://tonic-test-trackio-test.hf.space',
+                    'trackio_token': None,
+                    'log_artifacts': True,
+                    'log_metrics': True,
+                    'log_config': True,
+                    'experiment_name': 'petite-elle-l-aime-3-1',
+                    'dataset_name': 'legmlai/openhermes-fr',
+                    'dataset_split': 'train',
+                    'input_field': 'prompt',
+                    'target_field': 'accepted_completion',
+                    'filter_bad_entries': True,
+                    'bad_entry_field': 'bad_entry',
+                    'packing': False,
+                    'max_prompt_length': 12288,
+                    'max_completion_length': 8192,
+                    'truncation': True,
+                    'dataloader_num_workers': 10,
+                    'dataloader_pin_memory': True,
+                    'dataloader_prefetch_factor': 3,
+                    'max_grad_norm': 1.0,
+                    'group_by_length': True
+                },
+                'artifacts': [],
+                'logs': []
+            }
+        }
+        
+        self.experiments = backup_experiments
+        self.current_experiment = 'exp_20250720_134319'
+        logger.info(f"✅ Loaded {len(backup_experiments)} backup experiments")
+    
+    def _save_experiments(self):
+        """Save experiments to HF Dataset"""
+        try:
+            if self.hf_token:
+                from datasets import Dataset
+                from huggingface_hub import HfApi
+                
+                # Convert experiments to dataset format
+                dataset_data = []
+                for exp_id, exp_data in self.experiments.items():
+                    dataset_data.append({
+                        'experiment_id': exp_id,
+                        'name': exp_data.get('name', ''),
+                        'description': exp_data.get('description', ''),
+                        'created_at': exp_data.get('created_at', ''),
+                        'status': exp_data.get('status', 'running'),
+                        'metrics': json.dumps(exp_data.get('metrics', [])),
+                        'parameters': json.dumps(exp_data.get('parameters', {})),
+                        'artifacts': json.dumps(exp_data.get('artifacts', [])),
+                        'logs': json.dumps(exp_data.get('logs', [])),
+                        'last_updated': datetime.now().isoformat()
+                    })
+                
+                # Create dataset
+                dataset = Dataset.from_list(dataset_data)
+                
+                # Push to HF Hub
+                api = HfApi(token=self.hf_token)
+                dataset.push_to_hub(
+                    self.dataset_repo,
+                    token=self.hf_token,
+                    private=True  # Make it private for security
+                )
+                
+                logger.info(f"✅ Saved {len(dataset_data)} experiments to {self.dataset_repo}")
+                
+            else:
+                logger.warning("⚠️ No HF_TOKEN available, experiments not saved to dataset")
+                
+        except Exception as e:
+            logger.error(f"Failed to save experiments to dataset: {e}")
+            # Fall back to local file for backup
+            try:
+                data = {
+                    'experiments': self.experiments,
+                    'current_experiment': self.current_experiment,
+                    'last_updated': datetime.now().isoformat()
+                }
+                with open("trackio_experiments_backup.json", 'w') as f:
+                    json.dump(data, f, indent=2, default=str)
+                logger.info("✅ Saved backup to local file")
+            except Exception as backup_e:
+                logger.error(f"Failed to save backup: {backup_e}")
+    
+    def create_experiment(self, name: str, description: str = "") -> Dict[str, Any]:
+        """Create a new experiment"""
+        experiment_id = f"exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
+        
+        experiment = {
+            'id': experiment_id,
+            'name': name,
+            'description': description,
+            'created_at': datetime.now().isoformat(),
+            'status': 'running',
+            'metrics': [],
+            'parameters': {},
+            'artifacts': [],
+            'logs': []
+        }
+        
+        self.experiments[experiment_id] = experiment
+        self.current_experiment = experiment_id
+        self._save_experiments()
+        
+        logger.info(f"Created experiment: {experiment_id} - {name}")
+        return experiment
+    
+    def log_metrics(self, experiment_id: str, metrics: Dict[str, Any], step: Optional[int] = None):
+        """Log metrics for an experiment"""
+        if experiment_id not in self.experiments:
+            raise ValueError(f"Experiment {experiment_id} not found")
+        
+        metric_entry = {
+            'timestamp': datetime.now().isoformat(),
+            'step': step,
+            'metrics': metrics
+        }
+        
+        self.experiments[experiment_id]['metrics'].append(metric_entry)
+        self._save_experiments()
+        logger.info(f"Logged metrics for experiment {experiment_id}: {metrics}")
+    
+    def log_parameters(self, experiment_id: str, parameters: Dict[str, Any]):
+        """Log parameters for an experiment"""
+        if experiment_id not in self.experiments:
+            raise ValueError(f"Experiment {experiment_id} not found")
+        
+        self.experiments[experiment_id]['parameters'].update(parameters)
+        self._save_experiments()
+        logger.info(f"Logged parameters for experiment {experiment_id}: {parameters}")
+    
+    def log_artifact(self, experiment_id: str, artifact_name: str, artifact_data: str):
+        """Log an artifact for an experiment"""
+        if experiment_id not in self.experiments:
+            raise ValueError(f"Experiment {experiment_id} not found")
+        
+        artifact_entry = {
+            'name': artifact_name,
+            'timestamp': datetime.now().isoformat(),
+            'data': artifact_data
+        }
+        
+        self.experiments[experiment_id]['artifacts'].append(artifact_entry)
+        self._save_experiments()
+        logger.info(f"Logged artifact for experiment {experiment_id}: {artifact_name}")
+    
+    def get_experiment(self, experiment_id: str) -> Optional[Dict[str, Any]]:
+        """Get experiment details"""
+        return self.experiments.get(experiment_id)
+    
+    def list_experiments(self) -> Dict[str, Any]:
+        """List all experiments"""
+        return {
+            'experiments': list(self.experiments.keys()),
+            'current_experiment': self.current_experiment,
+            'total_experiments': len(self.experiments)
+        }
+    
+    def update_experiment_status(self, experiment_id: str, status: str):
+        """Update experiment status"""
+        if experiment_id in self.experiments:
+            self.experiments[experiment_id]['status'] = status
+            self._save_experiments()
+            logger.info(f"Updated experiment {experiment_id} status to {status}")
+    
+    def get_metrics_dataframe(self, experiment_id: str) -> pd.DataFrame:
+        """Get metrics as a pandas DataFrame for plotting"""
+        if experiment_id not in self.experiments:
+            return pd.DataFrame()
+        
+        experiment = self.experiments[experiment_id]
+        if not experiment['metrics']:
+            return pd.DataFrame()
+        
+        # Convert metrics to DataFrame
+        data = []
+        for metric_entry in experiment['metrics']:
+            step = metric_entry.get('step', 0)
+            timestamp = metric_entry.get('timestamp', '')
+            metrics = metric_entry.get('metrics', {})
+            
+            row = {'step': step, 'timestamp': timestamp}
+            row.update(metrics)
+            data.append(row)
+        
+        return pd.DataFrame(data)
+
+# Global instance
+trackio_space = TrackioSpace()
+
+def update_trackio_config(hf_token: str, dataset_repo: str) -> str:
+    """Update TrackioSpace configuration with new HF token and dataset repository"""
+    global trackio_space
+    
+    try:
+        # Create new instance with updated configuration
+        trackio_space = TrackioSpace(hf_token=hf_token if hf_token.strip() else None, 
+                                   dataset_repo=dataset_repo if dataset_repo.strip() else None)
+        
+        # Reload experiments with new configuration
+        trackio_space._load_experiments()
+        
+        return f"✅ Configuration updated successfully!\n📊 Dataset: {trackio_space.dataset_repo}\n🔑 HF Token: {'Set' if trackio_space.hf_token else 'Not set'}\n📈 Loaded {len(trackio_space.experiments)} experiments"
+        
+    except Exception as e:
+        return f"❌ Failed to update configuration: {str(e)}"
+
+def test_dataset_connection(hf_token: str, dataset_repo: str) -> str:
+    """Test connection to HF Dataset repository"""
+    try:
+        if not hf_token.strip():
+            return "❌ Please provide a Hugging Face token"
+        
+        if not dataset_repo.strip():
+            return "❌ Please provide a dataset repository"
+        
+        from datasets import load_dataset
+        
+        # Test loading the dataset
+        dataset = load_dataset(dataset_repo, token=hf_token)
+        
+        # Count experiments
+        experiment_count = len(dataset['train']) if 'train' in dataset else 0
+        
+        return f"✅ Connection successful!\n📊 Dataset: {dataset_repo}\n📈 Found {experiment_count} experiments\n🔗 Dataset URL: https://huggingface.co/datasets/{dataset_repo}"
+        
+    except Exception as e:
+        return f"❌ Connection failed: {str(e)}\n\n💡 Troubleshooting:\n1. Check your HF token is correct\n2. Verify the dataset repository exists\n3. Ensure your token has read access to the dataset"
+
+def create_dataset_repository(hf_token: str, dataset_repo: str) -> str:
+    """Create HF Dataset repository if it doesn't exist"""
+    try:
+        if not hf_token.strip():
+            return "❌ Please provide a Hugging Face token"
+        
+        if not dataset_repo.strip():
+            return "❌ Please provide a dataset repository"
+        
+        from datasets import Dataset
+        from huggingface_hub import HfApi
+        
+        # Parse username and dataset name
+        if '/' not in dataset_repo:
+            return "❌ Dataset repository must be in format: username/dataset-name"
+        
+        username, dataset_name = dataset_repo.split('/', 1)
+        
+        # Create API client
+        api = HfApi(token=hf_token)
+        
+        # Check if dataset exists
+        try:
+            api.dataset_info(dataset_repo)
+            return f"✅ Dataset {dataset_repo} already exists!"
+        except:
+            # Dataset doesn't exist, create it
+            pass
+        
+        # Create empty dataset
+        empty_dataset = Dataset.from_dict({
+            'experiment_id': [],
+            'name': [],
+            'description': [],
+            'created_at': [],
+            'status': [],
+            'metrics': [],
+            'parameters': [],
+            'artifacts': [],
+            'logs': [],
+            'last_updated': []
+        })
+        
+        # Push to hub
+        empty_dataset.push_to_hub(
+            dataset_repo,
+            token=hf_token,
+            private=True
+        )
+        
+        return f"✅ Dataset {dataset_repo} created successfully!\n🔗 View at: https://huggingface.co/datasets/{dataset_repo}\n📊 Ready to store experiments"
+        
+    except Exception as e:
+        return f"❌ Failed to create dataset: {str(e)}\n\n💡 Troubleshooting:\n1. Check your HF token has write permissions\n2. Verify the username in the repository name\n3. Ensure the dataset name is valid"
+
+# Initialize API client for remote data
+api_client = None
+try:
+    from trackio_api_client import TrackioAPIClient
+    api_client = TrackioAPIClient("https://tonic-test-trackio-test.hf.space")
+    logger.info("✅ API client initialized for remote data access")
+except ImportError:
+    logger.warning("⚠️ API client not available, using local data only")
+
+# Add Hugging Face Spaces compatibility
+def is_huggingface_spaces():
+    """Check if running on Hugging Face Spaces"""
+    return os.environ.get('SPACE_ID') is not None
+
+def get_persistent_data_path():
+    """Get a persistent data path for Hugging Face Spaces"""
+    if is_huggingface_spaces():
+        # Use a path that might persist better on HF Spaces
+        return "/tmp/trackio_experiments.json"
+    else:
+        return "trackio_experiments.json"
+
+# Override the data file path for HF Spaces
+if is_huggingface_spaces():
+    logger.info("🚀 Running on Hugging Face Spaces - using persistent storage")
+    trackio_space.data_file = get_persistent_data_path()
+
+def get_remote_experiment_data(experiment_id: str) -> Dict[str, Any]:
+    """Get experiment data from remote API"""
+    if api_client is None:
+        return None
+    
+    try:
+        # Get experiment details from API
+        details_result = api_client.get_experiment_details(experiment_id)
+        if "success" in details_result:
+            return {"remote": True, "data": details_result["data"]}
+        else:
+            logger.warning(f"Failed to get remote data for {experiment_id}: {details_result}")
+            return None
+    except Exception as e:
+        logger.error(f"Error getting remote data: {e}")
+        return None
+
+def parse_remote_metrics_data(experiment_details: str) -> pd.DataFrame:
+    """Parse metrics data from remote experiment details"""
+    try:
+        # Look for metrics in the experiment details
+        lines = experiment_details.split('\n')
+        metrics_data = []
+        
+        for line in lines:
+            if 'Step:' in line and 'Metrics:' in line:
+                # Extract step and metrics from the line
+                try:
+                    # Parse step number
+                    step_part = line.split('Step:')[1].split('Metrics:')[0].strip()
+                    step = int(step_part)
+                    
+                    # Parse metrics JSON
+                    metrics_part = line.split('Metrics:')[1].strip()
+                    metrics = json.loads(metrics_part)
+                    
+                    # Add timestamp
+                    row = {'step': step, 'timestamp': datetime.now().isoformat()}
+                    row.update(metrics)
+                    metrics_data.append(row)
+                    
+                except (ValueError, json.JSONDecodeError) as e:
+                    logger.warning(f"Failed to parse metrics line: {line} - {e}")
+                    continue
+        
+        if metrics_data:
+            return pd.DataFrame(metrics_data)
+        else:
+            return pd.DataFrame()
+            
+    except Exception as e:
+        logger.error(f"Error parsing remote metrics: {e}")
+        return pd.DataFrame()
+
+def get_metrics_dataframe(experiment_id: str) -> pd.DataFrame:
+    """Get metrics as a pandas DataFrame for plotting - tries remote first, then local"""
+    # Try to get remote data first
+    remote_data = get_remote_experiment_data(experiment_id)
+    if remote_data:
+        logger.info(f"Using remote data for {experiment_id}")
+        # Parse the remote experiment details to extract metrics
+        df = parse_remote_metrics_data(remote_data["data"])
+        if not df.empty:
+            logger.info(f"Found {len(df)} metrics entries from remote data")
+            return df
+        else:
+            logger.warning(f"No metrics found in remote data for {experiment_id}")
+    
+    # Fall back to local data
+    logger.info(f"Using local data for {experiment_id}")
+    return trackio_space.get_metrics_dataframe(experiment_id)
+
+def create_experiment_interface(name: str, description: str) -> str:
+    """Create a new experiment"""
+    try:
+        experiment = trackio_space.create_experiment(name, description)
+        return f"✅ Experiment created successfully!\nID: {experiment['id']}\nName: {experiment['name']}\nStatus: {experiment['status']}"
+    except Exception as e:
+        return f"❌ Error creating experiment: {str(e)}"
+
+def log_metrics_interface(experiment_id: str, metrics_json: str, step: str) -> str:
+    """Log metrics for an experiment"""
+    try:
+        metrics = json.loads(metrics_json)
+        step_int = int(step) if step else None
+        trackio_space.log_metrics(experiment_id, metrics, step_int)
+        return f"✅ Metrics logged successfully for experiment {experiment_id}\nStep: {step_int}\nMetrics: {json.dumps(metrics, indent=2)}"
+    except Exception as e:
+        return f"❌ Error logging metrics: {str(e)}"
+
+def log_parameters_interface(experiment_id: str, parameters_json: str) -> str:
+    """Log parameters for an experiment"""
+    try:
+        parameters = json.loads(parameters_json)
+        trackio_space.log_parameters(experiment_id, parameters)
+        return f"✅ Parameters logged successfully for experiment {experiment_id}\nParameters: {json.dumps(parameters, indent=2)}"
+    except Exception as e:
+        return f"❌ Error logging parameters: {str(e)}"
+
+def get_experiment_details(experiment_id: str) -> str:
+    """Get experiment details"""
+    try:
+        experiment = trackio_space.get_experiment(experiment_id)
+        if experiment:
+            # Format the output nicely
+            details = f"""
+📊 EXPERIMENT DETAILS
+====================
+ID: {experiment['id']}
+Name: {experiment['name']}
+Description: {experiment['description']}
+Status: {experiment['status']}
+Created: {experiment['created_at']}
+
+📈 METRICS COUNT: {len(experiment['metrics'])}
+📋 PARAMETERS COUNT: {len(experiment['parameters'])}
+📦 ARTIFACTS COUNT: {len(experiment['artifacts'])}
+
+🔧 PARAMETERS:
+{json.dumps(experiment['parameters'], indent=2)}
+
+📊 LATEST METRICS:
+"""
+            if experiment['metrics']:
+                latest_metrics = experiment['metrics'][-1]
+                details += f"Step: {latest_metrics.get('step', 'N/A')}\n"
+                details += f"Timestamp: {latest_metrics.get('timestamp', 'N/A')}\n"
+                details += f"Metrics: {json.dumps(latest_metrics.get('metrics', {}), indent=2)}"
+            else:
+                details += "No metrics logged yet."
+            
+            return details
+        else:
+            return f"❌ Experiment {experiment_id} not found"
+    except Exception as e:
+        return f"❌ Error getting experiment details: {str(e)}"
+
+def list_experiments_interface() -> str:
+    """List all experiments with details"""
+    try:
+        experiments_info = trackio_space.list_experiments()
+        experiments = trackio_space.experiments
+        
+        if not experiments:
+            return "📭 No experiments found. Create one first!"
+        
+        result = f"📋 EXPERIMENTS OVERVIEW\n{'='*50}\n"
+        result += f"Total Experiments: {len(experiments)}\n"
+        result += f"Current Experiment: {experiments_info['current_experiment']}\n\n"
+        
+        for exp_id, exp_data in experiments.items():
+            status_emoji = {
+                'running': '🟢',
+                'completed': '✅',
+                'failed': '❌',
+                'paused': '⏸️'
+            }.get(exp_data['status'], '❓')
+            
+            result += f"{status_emoji} {exp_id}\n"
+            result += f"   Name: {exp_data['name']}\n"
+            result += f"   Status: {exp_data['status']}\n"
+            result += f"   Created: {exp_data['created_at']}\n"
+            result += f"   Metrics: {len(exp_data['metrics'])} entries\n"
+            result += f"   Parameters: {len(exp_data['parameters'])} entries\n"
+            result += f"   Artifacts: {len(exp_data['artifacts'])} entries\n\n"
+        
+        return result
+    except Exception as e:
+        return f"❌ Error listing experiments: {str(e)}"
+
+def update_experiment_status_interface(experiment_id: str, status: str) -> str:
+    """Update experiment status"""
+    try:
+        trackio_space.update_experiment_status(experiment_id, status)
+        return f"✅ Experiment {experiment_id} status updated to {status}"
+    except Exception as e:
+        return f"❌ Error updating experiment status: {str(e)}"
+
+def create_metrics_plot(experiment_id: str, metric_name: str = "loss") -> go.Figure:
+    """Create a plot for a specific metric"""
+    try:
+        df = get_metrics_dataframe(experiment_id)
+        if df.empty:
+            # Return empty plot
+            fig = go.Figure()
+            fig.add_annotation(
+                text="No metrics data available",
+                xref="paper", yref="paper",
+                x=0.5, y=0.5, showarrow=False
+            )
+            return fig
+        
+        if metric_name not in df.columns:
+            # Show available metrics
+            available_metrics = [col for col in df.columns if col not in ['step', 'timestamp']]
+            fig = go.Figure()
+            fig.add_annotation(
+                text=f"Available metrics: {', '.join(available_metrics)}",
+                xref="paper", yref="paper",
+                x=0.5, y=0.5, showarrow=False
+            )
+            return fig
+        
+        fig = px.line(df, x='step', y=metric_name, title=f'{metric_name} over time')
+        fig.update_layout(
+            xaxis_title="Training Step",
+            yaxis_title=metric_name.title(),
+            hovermode='x unified'
+        )
+        return fig
+    
+    except Exception as e:
+        fig = go.Figure()
+        fig.add_annotation(
+            text=f"Error creating plot: {str(e)}",
+            xref="paper", yref="paper",
+            x=0.5, y=0.5, showarrow=False
+        )
+        return fig
+
+def create_experiment_comparison(experiment_ids: str) -> go.Figure:
+    """Compare multiple experiments"""
+    try:
+        exp_ids = [exp_id.strip() for exp_id in experiment_ids.split(',')]
+        
+        fig = go.Figure()
+        
+        for exp_id in exp_ids:
+            df = get_metrics_dataframe(exp_id)
+            if not df.empty and 'loss' in df.columns:
+                fig.add_trace(go.Scatter(
+                    x=df['step'],
+                    y=df['loss'],
+                    mode='lines+markers',
+                    name=f"{exp_id} - Loss",
+                    line=dict(width=2)
+                ))
+        
+        fig.update_layout(
+            title="Experiment Comparison - Loss",
+            xaxis_title="Training Step",
+            yaxis_title="Loss",
+            hovermode='x unified'
+        )
+        
+        return fig
+    
+    except Exception as e:
+        fig = go.Figure()
+        fig.add_annotation(
+            text=f"Error creating comparison: {str(e)}",
+            xref="paper", yref="paper",
+            x=0.5, y=0.5, showarrow=False
+        )
+        return fig
+
+def simulate_training_data(experiment_id: str):
+    """Simulate training data for demonstration"""
+    try:
+        # Simulate some realistic training metrics
+        for step in range(0, 1000, 50):
+            # Simulate loss decreasing over time
+            loss = 2.0 * np.exp(-step / 500) + 0.1 * np.random.random()
+            accuracy = 0.3 + 0.6 * (1 - np.exp(-step / 300)) + 0.05 * np.random.random()
+            lr = 3.5e-6 * (0.9 ** (step // 200))
+            
+            metrics = {
+                "loss": round(loss, 4),
+                "accuracy": round(accuracy, 4),
+                "learning_rate": round(lr, 8),
+                "gpu_memory": round(20 + 5 * np.random.random(), 2),
+                "training_time": round(0.5 + 0.2 * np.random.random(), 3)
+            }
+            
+            trackio_space.log_metrics(experiment_id, metrics, step)
+        
+        return f"✅ Simulated training data for experiment {experiment_id}\nAdded 20 metric entries (steps 0-950)"
+    except Exception as e:
+        return f"❌ Error simulating data: {str(e)}"
+
+def create_demo_experiment():
+    """Create a demo experiment with training data"""
+    try:
+        # Create demo experiment
+        experiment = trackio_space.create_experiment(
+            "demo_smollm3_training",
+            "Demo experiment with simulated training data"
+        )
+        
+        experiment_id = experiment['id']
+        
+        # Add some demo parameters
+        parameters = {
+            "model_name": "HuggingFaceTB/SmolLM3-3B",
+            "batch_size": 8,
+            "learning_rate": 3.5e-6,
+            "max_iters": 18000,
+            "mixed_precision": "bf16",
+            "dataset": "legmlai/openhermes-fr"
+        }
+        trackio_space.log_parameters(experiment_id, parameters)
+        
+        # Add demo training data
+        simulate_training_data(experiment_id)
+        
+        return f"✅ Demo experiment created: {experiment_id}\nYou can now test the visualization with this experiment!"
+    except Exception as e:
+        return f"❌ Error creating demo experiment: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="Trackio - Experiment Tracking", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🚀 Trackio Experiment Tracking & Monitoring")
+    gr.Markdown("Monitor and track your ML experiments with real-time visualization!")
+    
+    with gr.Tabs():
+        # Configuration Tab
+        with gr.Tab("⚙️ Configuration"):
+            gr.Markdown("### Configure HF Datasets Connection")
+            gr.Markdown("Set your Hugging Face token and dataset repository for persistent experiment storage.")
+            
+            with gr.Row():
+                with gr.Column():
+                    hf_token_input = gr.Textbox(
+                        label="Hugging Face Token",
+                        placeholder="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx",
+                        type="password",
+                        info="Your HF token for dataset access (optional - will use environment variable if not set)"
+                    )
+                    dataset_repo_input = gr.Textbox(
+                        label="Dataset Repository",
+                        placeholder="your-username/your-dataset-name",
+                        value="tonic/trackio-experiments",
+                        info="HF Dataset repository for experiment storage"
+                    )
+                    
+                    with gr.Row():
+                        update_config_btn = gr.Button("Update Configuration", variant="primary")
+                        test_connection_btn = gr.Button("Test Connection", variant="secondary")
+                        create_repo_btn = gr.Button("Create Dataset", variant="success")
+                    
+                    gr.Markdown("### Current Configuration")
+                    current_config_output = gr.Textbox(
+                        label="Status",
+                        lines=8,
+                        interactive=False,
+                        value=f"📊 Dataset: {trackio_space.dataset_repo}\n🔑 HF Token: {'Set' if trackio_space.hf_token else 'Not set'}\n📈 Experiments: {len(trackio_space.experiments)}"
+                    )
+                
+                with gr.Column():
+                    gr.Markdown("### Configuration Help")
+                    gr.Markdown("""
+                    **Getting Your HF Token:**
+                    1. Go to [Hugging Face Settings](https://huggingface.co/settings/tokens)
+                    2. Click "New token"
+                    3. Give it a name (e.g., "Trackio Access")
+                    4. Select "Write" permissions
+                    5. Copy the token and paste it above
+                    
+                    **Dataset Repository:**
+                    - Format: `username/dataset-name`
+                    - Examples: `tonic/trackio-experiments`, `your-username/my-experiments`
+                    - Use "Create Dataset" button to create a new repository
+                    
+                    **Environment Variables:**
+                    You can also set these as environment variables:
+                    - `HF_TOKEN`: Your Hugging Face token
+                    - `TRACKIO_DATASET_REPO`: Dataset repository
+                    
+                    **Actions:**
+                    - **Update Configuration**: Apply new settings and reload experiments
+                    - **Test Connection**: Verify access to the dataset repository
+                    - **Create Dataset**: Create a new dataset repository if it doesn't exist
+                    """)
+            
+            update_config_btn.click(
+                update_trackio_config,
+                inputs=[hf_token_input, dataset_repo_input],
+                outputs=current_config_output
+            )
+            
+            test_connection_btn.click(
+                test_dataset_connection,
+                inputs=[hf_token_input, dataset_repo_input],
+                outputs=current_config_output
+            )
+            
+            create_repo_btn.click(
+                create_dataset_repository,
+                inputs=[hf_token_input, dataset_repo_input],
+                outputs=current_config_output
+            )
+        
+        # Create Experiment Tab
+        with gr.Tab("Create Experiment"):
+            gr.Markdown("### Create a New Experiment")
+            with gr.Row():
+                with gr.Column():
+                    experiment_name = gr.Textbox(
+                        label="Experiment Name",
+                        placeholder="my_smollm3_finetune",
+                        value="smollm3_finetune"
+                    )
+                    experiment_description = gr.Textbox(
+                        label="Description",
+                        placeholder="Fine-tuning SmolLM3 model on custom dataset",
+                        value="SmolLM3 fine-tuning experiment"
+                    )
+                    create_btn = gr.Button("Create Experiment", variant="primary")
+                
+                with gr.Column():
+                    create_output = gr.Textbox(
+                        label="Result",
+                        lines=5,
+                        interactive=False
+                    )
+            
+            create_btn.click(
+                create_experiment_interface,
+                inputs=[experiment_name, experiment_description],
+                outputs=create_output
+            )
+        
+        # Log Metrics Tab
+        with gr.Tab("Log Metrics"):
+            gr.Markdown("### Log Training Metrics")
+            with gr.Row():
+                with gr.Column():
+                    metrics_exp_id = gr.Textbox(
+                        label="Experiment ID",
+                        placeholder="exp_20231201_143022"
+                    )
+                    metrics_json = gr.Textbox(
+                        label="Metrics (JSON)",
+                        placeholder='{"loss": 0.5, "accuracy": 0.85, "learning_rate": 2e-5}',
+                        value='{"loss": 0.5, "accuracy": 0.85, "learning_rate": 2e-5, "gpu_memory": 22.5}'
+                    )
+                    metrics_step = gr.Textbox(
+                        label="Step (optional)",
+                        placeholder="100"
+                    )
+                    log_metrics_btn = gr.Button("Log Metrics", variant="primary")
+                
+                with gr.Column():
+                    metrics_output = gr.Textbox(
+                        label="Result",
+                        lines=5,
+                        interactive=False
+                    )
+            
+            log_metrics_btn.click(
+                log_metrics_interface,
+                inputs=[metrics_exp_id, metrics_json, metrics_step],
+                outputs=metrics_output
+            )
+        
+        # Log Parameters Tab
+        with gr.Tab("Log Parameters"):
+            gr.Markdown("### Log Experiment Parameters")
+            with gr.Row():
+                with gr.Column():
+                    params_exp_id = gr.Textbox(
+                        label="Experiment ID",
+                        placeholder="exp_20231201_143022"
+                    )
+                    parameters_json = gr.Textbox(
+                        label="Parameters (JSON)",
+                        placeholder='{"learning_rate": 2e-5, "batch_size": 4}',
+                        value='{"learning_rate": 3.5e-6, "batch_size": 8, "model_name": "HuggingFaceTB/SmolLM3-3B", "max_iters": 18000, "mixed_precision": "bf16"}'
+                    )
+                    log_params_btn = gr.Button("Log Parameters", variant="primary")
+                
+                with gr.Column():
+                    params_output = gr.Textbox(
+                        label="Result",
+                        lines=5,
+                        interactive=False
+                    )
+            
+            log_params_btn.click(
+                log_parameters_interface,
+                inputs=[params_exp_id, parameters_json],
+                outputs=params_output
+            )
+        
+        # View Experiments Tab
+        with gr.Tab("View Experiments"):
+            gr.Markdown("### View Experiment Details")
+            with gr.Row():
+                with gr.Column():
+                    view_exp_id = gr.Textbox(
+                        label="Experiment ID",
+                        placeholder="exp_20231201_143022"
+                    )
+                    view_btn = gr.Button("View Experiment", variant="primary")
+                    list_btn = gr.Button("List All Experiments", variant="secondary")
+                
+                with gr.Column():
+                    view_output = gr.Textbox(
+                        label="Experiment Details",
+                        lines=20,
+                        interactive=False
+                    )
+            
+            view_btn.click(
+                get_experiment_details,
+                inputs=[view_exp_id],
+                outputs=view_output
+            )
+            
+            list_btn.click(
+                list_experiments_interface,
+                inputs=[],
+                outputs=view_output
+            )
+        
+        # Visualization Tab
+        with gr.Tab("📊 Visualizations"):
+            gr.Markdown("### Training Metrics Visualization")
+            with gr.Row():
+                with gr.Column():
+                    plot_exp_id = gr.Textbox(
+                        label="Experiment ID",
+                        placeholder="exp_20231201_143022"
+                    )
+                    metric_dropdown = gr.Dropdown(
+                        label="Metric to Plot",
+                        choices=["loss", "accuracy", "learning_rate", "gpu_memory", "training_time"],
+                        value="loss"
+                    )
+                    plot_btn = gr.Button("Create Plot", variant="primary")
+                
+                with gr.Column():
+                    plot_output = gr.Plot(label="Training Metrics")
+            
+            plot_btn.click(
+                create_metrics_plot,
+                inputs=[plot_exp_id, metric_dropdown],
+                outputs=plot_output
+            )
+            
+            gr.Markdown("### Experiment Comparison")
+            with gr.Row():
+                with gr.Column():
+                    comparison_exp_ids = gr.Textbox(
+                        label="Experiment IDs (comma-separated)",
+                        placeholder="exp_1,exp_2,exp_3"
+                    )
+                    comparison_btn = gr.Button("Compare Experiments", variant="primary")
+                
+                with gr.Column():
+                    comparison_plot = gr.Plot(label="Experiment Comparison")
+            
+            comparison_btn.click(
+                create_experiment_comparison,
+                inputs=[comparison_exp_ids],
+                outputs=comparison_plot
+            )
+        
+        # Demo Data Tab
+        with gr.Tab("🎯 Demo Data"):
+            gr.Markdown("### Generate Demo Training Data")
+            gr.Markdown("Use this to simulate training data for testing the interface")
+            with gr.Row():
+                with gr.Column():
+                    demo_exp_id = gr.Textbox(
+                        label="Experiment ID",
+                        placeholder="exp_20231201_143022"
+                    )
+                    demo_btn = gr.Button("Generate Demo Data", variant="primary")
+                    create_demo_btn = gr.Button("Create Demo Experiment", variant="secondary")
+                
+                with gr.Column():
+                    demo_output = gr.Textbox(
+                        label="Result",
+                        lines=5,
+                        interactive=False
+                    )
+            
+            demo_btn.click(
+                simulate_training_data,
+                inputs=[demo_exp_id],
+                outputs=demo_output
+            )
+            
+            create_demo_btn.click(
+                create_demo_experiment,
+                inputs=[],
+                outputs=demo_output
+            )
+        
+        # Update Status Tab
+        with gr.Tab("Update Status"):
+            gr.Markdown("### Update Experiment Status")
+            with gr.Row():
+                with gr.Column():
+                    status_exp_id = gr.Textbox(
+                        label="Experiment ID",
+                        placeholder="exp_20231201_143022"
+                    )
+                    status_dropdown = gr.Dropdown(
+                        label="Status",
+                        choices=["running", "completed", "failed", "paused"],
+                        value="running"
+                    )
+                    update_status_btn = gr.Button("Update Status", variant="primary")
+                
+                with gr.Column():
+                    status_output = gr.Textbox(
+                        label="Result",
+                        lines=3,
+                        interactive=False
+                    )
+            
+            update_status_btn.click(
+                update_experiment_status_interface,
+                inputs=[status_exp_id, status_dropdown],
+                outputs=status_output
+            )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch() 

scripts/trackio_tonic/requirements.txt

@@ -0,0 +1,22 @@
+# Gradio and web interface
+gradio>=4.0.0
+gradio-client>=0.10.0
+
+# Core dependencies for Trackio Space
+requests>=2.31.0
+numpy>=1.24.0
+pandas>=2.0.0
+
+# JSON and data handling
+jsonschema>=4.17.0
+
+# Optional: for better UI
+plotly>=5.0.0
+pandas>=2.0.0
+numpy>=1.24.0
+datasets>=2.14.0
+huggingface-hub>=0.16.0
+requests>=2.31.0 
+
+# Development and debugging
+python-dotenv>=1.0.0